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Abstract

Objectives

This is the first systematic review and meta-analysis of the prevalence of multimorbidity, its risk factors including socioeconomic factors, and the consequences of multimorbidity on health systems and broader society in India.

Methods

A systematic review of both published and grey literature from five databases (Medline, Embase, EBSCO, Scopus, and ProQuest) was conducted including original studies documenting prevalence or patient outcomes associated with multimorbidity among adults in India. We excluded studies that did not explicitly mention multimorbidity. Three independent reviewers did primary screening based on titles and abstracts followed by full-text review for potential eligibility. The risk of bias was independently assessed by two reviewers following the Appraisal Tool for Cross-Sectional Studies. We presented both qualitative and quantitative (through meta-analysis) summaries of the evidence. The protocol for this study was prospectively registered with PROSPERO (CRD42021257281).

Results

The review identified 5442 articles out of which 35 articles were finally included in this study. Twenty-three studies were based on the primary data while 12 used secondary data. Eleven studies were conducted in hospital/primary care setting while 24 were community-based. The pooled prevalence of multimorbidity based on (n=19) studies included for meta-analysis was 20% (95% CI: 19% to 20%). The most frequent outcomes were increased healthcare utilization, reduced health-related quality of life, physical and mental functioning.

Conclusion

We identified a wide variance in the magnitude of multimorbidity across age groups and regions with most of the studies from eastern India. Nation-wide studies, studies on vulnerable populations and interventions are warranted.

Keywords

Multimorbidity India systematic review meta-analysis prevalence patient outcomes

Introduction

Multimorbidity, the co-occurrence of two or more chronic conditions in an individual is becoming a norm in low-and middle-income countries (LMICs).¹ This can be attributed to the rise in the ageing population along with an increase in non-communicable diseases (NCDs) vis-a-vis prevailing infectious diseases.² Multimorbidity encompasses all long term conditions (LTCs) including NCDs of chronic nature such as hypertension, diabetes or an infectious disease of longer duration such as filariasis or a mental health condition such as depression. This often leads to an increase in healthcare utilization, expenditure along with poorer patient outcomes.³ Further, it also deteriorates the health-related quality of life (HRQoL).⁴

In LMICs such as India, multimorbidity is on the rise; evident by a recent study that estimated a 55% prevalence among adults attending public healthcare facilities.⁵ However, the evidence on multimorbidity burden is scattered across various studies done in either small samples or particular regions, but no study to date has synthesized the national estimates. Although, the high prevalence indicates an additional burden on the already swamped healthcare system, the national estimate of multimorbidity burden could help in evidence based guidance to plan changes to the structure and delivery of primary care. Primary care is the first and foremost point of contact between multimorbid individuals and the healthcare systems.⁶ It is thus imperative to strengthen primary care along with healthcare advancements to mitigate multimorbidity and its challenges such as polypharmacy, and unplanned emergency healthcare use. This also lays a financial burden on families making them vulnerable to impoverishment.⁷ Additionally, multimorbid patients face convoluted care pathways as they navigate to multiple care providers and specialists for each of the condition.⁸ Moreover, the existing guidelines focus on single disease-based management which makes it challenging for the healthcare providers to manage multiple long-term conditions.⁸ Hence, multimorbidity and primary care research needs to be prioritized, for both resource and planning, and to identify effective interventions.

With emerging epidemiological trends of chronic conditions and transitioning demography, generating evidence on the burden of multimorbidity is new in Indian context.⁹ Although, multimorbidity has become a norm, still multimorbidity and primary care research has not gained pace in the country.⁹ A systematic review conducted in 2015, estimated 4.5% to 83% prevalence of multimorbidity in South Asia.¹⁰ Given the increasing prevalence, there is a pressing need to assess the burden and outcomes of multimorbidity in India. Moreover, India has the second-largest population in the world which in itself reflects the need for special attention. Recent programs such as Ayushman Bharat and the establishment of Ayushman Arogya Mandir formerly Health and Wellness Centres along with integrating AYUSH (Ayurveda, Yoga & Naturopathy, Unani, Siddha, Sowa Rigpa, Homoeopathy) system in the mainstream medical facilities shows India’s commitment to Universal Health Coverage (UHC) which could be given direction with a shred of cumulative evidence.¹¹ Nonetheless, various LMICs face similar situation in managing multimorbidity despite its rising prevalence. Additionally, the social determinants of multimorbidity remain alike for most of the LMICs and hence, identifying these determinants through the present review would generate relevant evidence for a wider population. These determinants can help in making the healthcare system aware and resilient towards the chronic care needs of local people. Therefore, this systematic review aimed to estimate the prevalence of multimorbidity, identify its risk factors including social determinants or consequences of multimorbidity in India.

Methods

Protocol and standards

This systematic review was prospectively registered with the International Prospective Register of Systematic Reviews (Registration ID: CRD42021257281).¹² It was performed and reported following Preferred Reporting Items for Systematic reviews and Meta-analysis (PRISMA) guidelines (Supplementary Table S1).¹³

Eligibility criteria

Original studies documenting prevalence, determinants or patient outcomes associated with multimorbidity; studies with participants aged ≥18 years; conducted either in a primary care/outpatient or community based setting from India were included. Studies in which multimorbidity was not explicitly defined; or included an index condition i.e. comorbidity were excluded. Additionally, systematic reviews, commentaries, editorials, newsletters and qualitative studies were also excluded.

Information sources and search strategy

We searched both medical literature databases and grey literature to make our search exhaustive. A comprehensive search was conducted using electronic databases Medline through PubMed and Embase along with EBSCO host and ProQuest. Google Scholar search engine was also used to retrieve articles. The reference list of included studies was hand-searched as an additional source of information.

The basic search syntax comprised of two concepts: multimorbidity and prevalence. We used PubMed to build the basic search strategy, although the individual search strategy for each database was developed separately. In PubMed, medical subject headings (MeSH) term for “multimorbidity” was entered in 2018.¹⁴ However, for other databases the terms specific to database such as Emtree for Embase along with various other keywords were used to make the search strategy comprehensive. Thereafter, search strategy was refined to include data from India only. The detailed search strategy used for each database is provided in Supplementary Table S2. We included articles published up until August 2021.

Study selection, data extraction and synthesis

The studies retrieved from various databases were merged and screened for duplicates. Primary screening based on the titles and abstracts (following inclusion and exclusion criteria) was done by three reviewers (RV, AS and MB) independently. The articles were categorized as relevant, irrelevant or unsure; if any article was marked as irrelevant by all three of the reviewers it was eliminated. In the next stage, full texts of all included articles from primary screening were reviewed by three independent reviewers for potential eligibility (RV, AS and MB). This round strictly followed inclusion and exclusion criteria based on which studies were finally included. Any dissent was resolved by forming a consensus with the help of another reviewer (SP).

Data from relevant studies were extracted using a preformed and piloted data extraction sheet and entered by three independent reviewers (RV, AS and MB). This data was assessed by another reviewer (SP) to check for disparities. Differences in the data extraction were resolved by the entire team in consensus. Additionally, if the data was not clear, we contacted the respective authors for further clarity. We extracted the following information from the included studies: author, journal title, year of publication, study design, study setting, age, sex, sample size, prevalence, patterns and patient outcomes of multimorbidity. Additionally, we also collated data on definition of multimorbidity used, tool used for measuring multimorbidity, self-reported or objectively assessed, total number and list of long term conditions included.

Risk of bias in individual studies

The risk of bias was independently assessed by two reviewers (RV, AS) following the Appraisal tool for Cross-Sectional Studies (AXIS) that addresses cross-sectional study reporting and overall quality.¹⁵ The AXIS tool comprehensively assesses studies based on twenty questions from the introduction (one question), methods (ten questions), results (five questions), discussion (two questions) and others (two questions). The AXIS tool covers the following key domains: study design, sample size, sampling method, and methods. Any potential differences between the reviewers were resolved by the third reviewer (SP). Each of the criteria was evaluated on its presence (“Yes” =1) or absence (“No”/ “Do not know”=0). The percentage of items presented as ‘yes’ was then calculated. Each question was scored with a possible score of one, with scores of each question summed to provide an overall score of twenty. The AXIS repartition: 0–50% has high risk of bias, 51–80% has medium risk of bias and 81–100% has low risk of bias.

Summary measures

The findings were summarized as qualitative and quantitative summary of the evidence. We qualitatively described the characteristics of the included studies whereas pooled prevalence estimates represented the quantitative summary. We used STATA version 17.0 (STATA Corp., Texas) for data analysis. We used ‘metan’ command to calculate the pooled prevalence based on random-effect model. The I² statistic was used to assess the variability between studies; this statistic can take values between 0% and 100%, with high values indicative of strong heterogeneity. We anticipated a high heterogeneity due to the nature of selected studies such as nationally representative samples vs. primary studies with relatively smaller sample size; and age group variations in the study i.e. 18 years and above or older adults like ≥45 years or ≥60 years. Hence, we planned sub group analysis based on the nature of data i.e. primary data or secondary data and age group considered i.e. ≥18 years or ≥45 years.

Patient and public involvement

We did not involve patients or the public in the design, or conduct, or reporting, or dissemination plans of our research.

Results

We retrieved 5442 articles from the electronic databases after excluding 584 duplicates. A total of 35 articles^5,16-49 met the inclusion criteria after primary as well as full-text screening, and quality appraisal (Figure 1).

Figure 1.

PRISMA flow diagram representing selection of studies included in systematic review.

General characteristics of selected studies

We observed 60.5% (n=23) of the included studies were based on primary data^{5,17,19,20,22,25-29,32-35,37,39-43,45-48} whereas 39.5% were from secondary data analysis of nationally representative samples such as Longitudinal Ageing Study in India (LASI) wave-0 (n=1),¹⁸ Study on Global Ageing and Adult Health (SAGE) wave-0¹⁵ (n=1), SAGE wave-1^{16,21,24,36,44} (n=5), LASI wave-1^32,35 (n=2), Building Knowledge Base on Population Aging in India (BKPAI)³⁸ (n=1), India Human Development Survey (IHDS-II)³¹ (n=1) and -United Nations Population Fund (UNFDPA)²³ (n=1). Fifteen studies were from Odisha^{5,17,19,25,26,28,32-34,39,40,42,45,47,48}; 12 studies^{16,18,21,23,24,30,31,36,38,44,46,49} represented pan India and eight studies were from other states of India.^{20,22,27,29,35,37,41,43} Eleven studies^{5,19,25,26,28,32,33,34,39,43,47} were conducted in hospital/primary care (study conducted among patients attending hospitals/primary care) setting while 24 were from the community based set up (data collected by visiting household through face to face interviews)^{16-18,20-24,27,29-31,35-38,40-42,44-46,48,49-49} (Table 1). All the studies defined the term multimorbidity. The number of conditions considered to assess multimorbidity ranged from 4 to 24 conditions with a median of 12 conditions (Supplementary Table S3). Hypertension and type-2 diabetes were uniformly included in all studies to assess multimorbidity. Two reports did not define the number of conditions included.^33,41 All the studies used self-reported questionnaires for the assessment of multimorbidity. The number of chronic conditions considered in the primary studies ranged from 9 to 24 (mean 17.1) whereas it ranged from 4 to 20 (mean 9.7) in studies published from secondary data.

Table 1.

Characteristics of the included studies.

Sl. No	Author, year	State	Region	Study Design	Study Period	Sample size	No. of conditions included	Age in years	%Female	Setting	Prevalence of Multimorbidity	Type of data	Assessment tool used
1.	Pati S et al, 2014¹⁶	Assam, Karnataka, Maharashtra, Rajasthan, Uttar Pradesh and West Bengal	NR	Cross-sectional	2007-2010	10973	9	18-49; ≥50	61.34	Community (Rural , urban)	977 (8.90%)	Secondary (SAGE-Wave-1)	Self-developed
2.	Banjare P et al, 2014¹⁷	Odisha	East India	Cross-sectional	2011-2012	310	20	≥60	52.33	Community (Rural)	177 (57% )	Primary	Self-developed
3.	Arokiasamy P et al, 2015¹⁸	Karnataka, Kerala, Punjab, Rajasthan	NR	Cross-sectional	2010	1683	7	≥45	56.03	Community (Rural , urban)	151 (8.97%)	Secondary (LASI Wave 0)	Self-rated Health, Activity of Daily Living
4.	Pati S et al.,2015¹⁹	Odisha	East India	Cross-sectional	2013 - 2014	1649	22	18-60	44.14	Primary care (Rural , urban)	467 (28.32%)	Primary	Multimorbidity Assessment Protocol
5.	Vadreru L, 2015²⁰	West Bengal	East India	Cross-sectional	2009	815	6	≥40	48.71	Community (Rural , urban)	NA	Primary	Self-developed
6.	Arokiasamy P, 2015²¹	Assam, Karnataka, Maharashtra, Rajasthan, Uttar Pradesh and West Bengal	NR	Cross-sectional	2007-2010	11230	8	≥18	49.10	Community (Rural , urban)	2471 (22%)	Secondary (SAGE-Wave-1)	Self-developed
7.	Gupta A et al, 2016²²	Punjab	North India	Cross-sectional	2014	534	13	≥60	54.30	Community (Urban)	352 (65.91%)	Primary	Self-developed
8.	Mini GK, et al 2016²³	Kerala, Tamil Nadu, South Punjab, Himachal Pradesh, Maharashtra, Odisha, West Bengal	NR	Cross-sectional	2011	9852	12	≥60	53.00	Community (Rural , urban)	3024 (30.7%)	Secondary (UNFDPA)	Self-developed
9.	Agrawal S et al, 2016²⁴	Assam, Karnataka, Maharashtra, Rajasthan, Uttar Pradesh and West Bengal	NR	Cross-sectional	2007-2010	12198	9	≥18	61.32	Community (Rural , urban)	2927 (23.99%)	Secondary (SAGE Wave-1)	Self-developed
10.	Pati S et al, 2016²⁵	Odisha	East	Cross-sectional	2014	103	18	≥18	44.66	Primary care (Rural , urban, semi-urban, Tribal)	24 (23.3%)	Primary	MAQ-PC
11.	Pati S et al, 2017²⁶	Odisha	East	Cross-sectional	2013	197	11	NA	34.01	Hospital (Urban)	94 (47.7%)	Primary	Self-developed
12.	Audinarayana N. et al, 2017²⁷	Tamil Nadu	South	Cross-sectional	2009-2011	778	12	≥60	53.21	Community (Urban)	475 (61.05%)	Primary	Self-developed
13.	Pati S et al.,2017²⁸	Odisha	East	Cross-sectional	2013-2014	1649	21	≥18	44.14	Primary care (Rural , urban)	467 (28.32%)	Primary	MAQ-PC
14.	Jain K et al, 2018²⁹	Rajasthan	North	Cross-sectional	2012-2013	400	20	≥50	38.25	Community (Urban)	124 (31%)	Primary	Self-developed
15.	Zhou C, et al, 2018³⁰	Assam, Karnataka, Maharashtra, Rajasthan, Uttar Pradesh and West Bengal	NR	Cross-sectional	2002-2004	9199	9	18-70	48.62	Community	3155 (34.3%)	Secondary (WHS/SAGE Wave 0)	Self-developed
16.	Muksor A et al, 2018³¹	All states/UTs	NR	Cross-sectional	2011-2013	1, 10,434	13	19-59	NA	Community (Rural , urban)	Rural: 651/72334 (0.9%) Urban: 609/ 38100 (1.6%)	Secondary (IHDS-II)	Self- developed
17.	Swain S et al, 2019³²	Odisha	East	Cross-sectional	2017	342	21	≥40	44.44	Primary care	215 (63%)	Primary	Semi-structured and Edmonton frailty score tool
18.	Palo SK et al, 2019³³	Odisha	East	Cross-sectional	2015	183	NA	NA	34.97	Primary care (Rural)	98 (53.5%)	Primary	Self-developed
19.	Pati S et al., 2019³⁴	Odisha	East	Cross-sectional	2013-2014	1649	21	≥18	44.14	Primary care (Rural, Urban)	467 (28.32%)	Primary	MAQ-PC
20.	Verma V t al, 2019³⁵	Uttar Pradesh	North India	Cross-sectional	2014-2015	400	9	≥60	46.25	Community (Rural, Urban)	212 (53%)	Primary	Self-developed
21.	Vancampfort D et al, 2019³⁶	Assam, Karnataka, Maharashtra, Rajasthan, Uttar Pradesh and West Bengal	NR	Cross-sectional	2007-2010	34129	11	≥50	52.09	Community (Rural, Urban)	14812 (43.4%)	Secondary (SAGE Wave-1)	Self-developed
22.	Rohini C et al, 2020³⁷	Kerala	South	Cross-sectional	2020	410	11	NA	59.75	Community (Rural)	186 (45.36%)	Primary	Self-developed
23.	Sathya T et al, 2020³⁸	Himachal Pradesh, Kerala, Maharashtra, Odisha, Punjab, Tamil Nadu and West Bengal	NR	Cross-sectional	2011	9852	20	≥60	5192	Community (Rural, Urban)	1823 (18.5%)	Secondary (BKPAI)	Self-rated health and Activity of daily living
24.	Pati S et al,2020³⁹	Odisha	East	Cross-sectional	2013-2014	1649	21	≥18	44.14	Primary care (Rural, Urban)	467 (28.32%)	Primary	MAQ-PC
25.	Kshatri JS et al.,2020⁴⁰	Odisha	East	Cross-sectional	2019-2020	725	18	≥60	47.86	Community (Rural)	354 (48.8%)	Primary	MAQ-PC
26.	Panda M et al, 2020⁴¹	Delhi	North	Cross-sectional	2018-2019	300	18	≥60	NA	Community (Slum)	NA	Primary	EDMONSTON Frail Scale
27.	Kshatri JS et al., 2020⁴²	Odisha	East	Cross-sectional	2019-2020	725	NA	60-106	47.86	Community (Rural)	354 (48.8%)	Primary	MAQ-PC, Frailty index for elderly, Instrumental activities of daily living
28.	Vargese S et al,2020⁴³	Kerala	South	Cross-sectional	2017	525	12	18 to 70	53.14	Primary care (Rural)	85 (16.2%)	Primary	Self-developed
29.	Bayes Marin I, 2020⁴⁴	Assam, Karnataka, Maharashtra, Rajasthan, Uttar Pradesh and West Bengal	NR	Cross-sectional	2007-2010	6558	8	≥18	49.60	Community (Rural, Urban)	NA	Secondary (SAGE Wave -1)	Self-developed
30.	Pati S et al, 2021⁴⁵	Odisha	East	Cross-sectional	2020	600	23	NA	51.00	Community (Rural, Urban)	237 (39.5%)	Primary	MAQ-COVID-19
31.	Srivastava S et al, 2021⁴⁶	All states except Sikkim	NR	Cross-sectional	2017-2018	31464	9	≥60	52.01	Community (Rural, Urban)	7551 (24%)	Secondary (LASI Wave-1)	Self -developed
32.	Pati S, et al., 2021⁴⁷	Odisha	East	Cross-sectional	2019	500	24	≥60	47.80	Hospital (Rural, Urban)	250 (50%)	Primary	MAQ-PsyC
33.	Kshatri JS, 2021⁴⁸	Odisha	East	Cross-sectional	2019-2020	725	21	≥18	47.86	Community (Rural)	354 (48.8%)	Primary	MAQ-PC
34.	Pati S et al, 2021⁵	Odisha	East	Cross-sectional	2015	1870	18	≥18	37.00	Primary care (Rural, Urban)	1028 (54.97%)	Primary	MAQ-PC
35.	Ansari S, 2021⁴⁹	All states	NR	Cross-sectional	2017-2018	31464	10	≥45	53.00	Community (Rural, Urban)	7403 (23.53%)	Secondary (LASI Wave-1)	Centre for Epidemiological Studies Depression Scale

NR: Nationally representative, UT: Union Territory, IHDS- India Human Development Survey, WHS-World Health Survey, SAGE-Study on Global Ageing and Adult Health, LASI-Longitudinal Ageing Study in India, UNFDPA-United Nations Population Fund, BKPAI- Building Knowledge Base on Population Aging in India, NSS -National Sample Survey, MAQ-PC: Multimorbidity Assessment Questionnaire for Primary Care, NA: Not Available.

Assessment of risk of bias

A total of 36 studies qualified for the quality check. We excluded one study which explored management of geriatric multimorbidity in old age home residents in India⁵⁰ as it scored only four points (20%). The quality appraisal of the remaining 35 included articles revealed that 15 studies scored 81-100% and were categorized as having low risk of bias,^{17,18,20,28,29,32,33,37,39,41-43,46-48} whereas 20 studies scored 51-80% and were grouped as having medium risk of bias^{5,16,19,21-27,30,31,34-36,38,40,44-45,49} (Supplementary Table S4).

Prevalence

The prevalence of multimorbidity was reported in 32 of the included studies and this ranged from 1.16% to 65.9% (Table 1). Three studies did not report the prevalence of multimorbidity.^20,41,44 The prevalence from twelve studies with a nationally representative sample ranged from 8.9% to 63.3% while the prevalence of multimorbidity from primary studies ranged from 16.2% to 65.9%.⁵ ^{17,19,20,22,25-29,32-35,37,39-43,45-48} The estimate of multimorbidity for Odisha (state with maximum studies, n=15)^{5,17,19,25,26,28,32-34,39,40,42,45,47,48} ranged from 23.3% to 63.06% whereas for other states the prevalence ranged from 16.2 % to 65.9%.^{20,22,27,29,35,37,41,43} Based on the age, among ten studies with a population of ≥60 years, the prevalence ranged from 30.7% to 65.9%. Similarly, the prevalence reported for studies that considered population aged ≥18 years (n=13) and ≥45 years (n=5) ranged from 1.16% to 55% and 9% to 63.06% respectively.

We included 19 studies for meta-analysis which gave a pooled prevalence of multimorbidity to be 20% (Figure 2). Separate meta-analysis was also conducted for studies reporting multimorbidity based on primary data which yielded the pooled prevalence of 43% (Supplementary Figure 1) while studies based on secondary data showed the pooled prevalence to be 4% (Supplementary Figure 2). We also conducted a separate meta-analysis on the basis of age group selected i.e. studies reporting multimorbidity among participants aged ≥18 years that showed the pooled prevalence of 14% (Supplementary Figure 3) while studies among respondents aged ≥45years showed the pooled prevalence of 23% (Supplementary Figure 4). The list of studies excluded from meta-analysis along with their reasons for exclusion is presented in Table 2.

Figure 2.

Pooled prevalence of multimorbidity in India.

Table 2.

List of studies excluded from meta-analysis along with their reasons for exclusion.

S. No	Author, Year	Reason for Exclusion
1	Vadreru L et al., 2015 ²⁰	Data not available
2	Arokiasamy P et al., 2015 ²¹	Data from SAGE wave-1 used, another study based on same data with larger sample included
3	Mini GK et al., 2016 ²³	Data from United Nations Population Fund used, another study based on same data included
4	Agrawal S et al., 2016 ²⁴	Data from SAGE wave-1 used, another study based on same data included
5	Pati S et al., 2017 ²⁸	Data same as Pati S et al, 2015 ¹⁹ (Included)
6	Zhou C et al., 2018 ³⁰	Data from SAGE wave-1 used, another study based on same data included
7	Muksor A et al., 2018 ³¹	Data is not clear as data is segregated based on urban and rural.
8	Swain S et al., 2019 ³²	Definition of multimorbidity not uniform as other studies (Multimorbidity defined as ≥3 conditions)
9	Pati S et al., 2019 ³⁴	Data same as Pati S et al, 2015 ¹⁹ (Included)
10	Vancampfort D et al, 2019 ³⁶	Data from SAGE wave-1 used; another study based on same data included
11	Pati S et al., 2020 ³⁹	Data same as Pati S et al, 2015 ¹⁹ (Included)
12	Panda M et al., 2020 ⁴¹	Data not available
13	Kshatri JS et al., 2020b ⁴²	Data same as Kshatri JS et al, 2020a ⁴⁰ (Included)
14	Bayes MI et al., 2020 ⁴⁴	Data not available
15	Kshatri JS et al., 2021 ⁴⁸	Data same as Kshatri JS et al, 2020a ⁴⁰ (Included)

Pattern

Nine studies^{17,23,26,28,33,37,40,44,47} gave information about the pattern of multimorbidity (Table 3). The commonly occurring disease clusters were the occurrence of two diseases or dyads (n=7)^{23,26,28,33,37,40,47} followed by triads (n=4).^17,28,37,40 The commonly identified dyads were arthritis and hypertension,diabetes and hypertension, hypertension and overweight, tuberculosis and ulcer disease. In one study²⁸ patterns across gender were described; male- acid peptic disease + arthritis (7.95%); female- acid peptic disease + hypertension (10.58%). Additionally, it also presented patterns based on age; younger age group i.e. 18 to 29 years were acid peptic disease with arthritis/ chronic backache/ tuberculosis /chronic lung disease, while older age groups (≥40 years) had more frequent combinations of hypertension + arthritis/ chronic lung disease/ vision difficulty, and arthritis + chronic backache. Bayes Marin⁴⁴ described the patterns based on classes as cardio-metabolic or respiratory-mental-articular. Cardio-metabolic was reported to be high 6.1% (5.3 to 6.8) in India (diabetes, hypertension, myocardial infarction, angina/stroke). The triad of acid peptic disease + arthritis + chronic backache was common in men in all age groups whereas women reported combinations of hypertension + chronic backache + arthritis.²⁸

Table 3.

Patterns of multimorbidity.

Author, Year^Reference	Pattern reported	Pattern description	^n/N (%) or (%)^
Banjare P et al, 2014¹⁷	Triad	Arthritis, Chronic Obstructive Pulmonary Disease (COPD), High Blood Pressure	8/164 (0.5)
Mini GK, et al 2016²³	Dyad	Arthritis and High Blood Pressure Arthritis and Cataract Diabetes and High Blood Pressure	7.5 5.3 4.7
Pati S et al, 2017²⁶	Dyad	Tuberculosis and Ulcer disease	3/197 (1.5)
Pati S et al., 2017²⁸	Dyad, and Triad	Dyad • Younger age group (18 to 29 years) Acid Peptic Disease with Arthritis/ Chronic Backache/Tuberculosis/ Chronic Lung Disease. • Older age group (≥40 years) Hypertension with Arthritis/Chronic Lung Disease/Visual Difficulty; and Arthritis with Chronic Backache • Male Acid Peptic Disease with Arthritis • Female Acid Peptic Disease with Hypertension Triad • Male • Acid peptic disease, Arthritis and Chronic Backache • Female Acid Peptic Disease, Arthritis and Chronic Backache	NA NA 7.95 10.58 3.64 4.12
Palo SK et al, 2019³³	Dyad	Diabetes and Hypertension	14.1
Rohini C et al, 2020³⁷	Dyad, and Triad	Dyad Diabetes and Hypertension Triad Diabetes, Hypertension and Ischemic Heart Disease	127/410 (30.9) 23/410 (5.6)
Kshatri JS et al.,2020⁴⁰	Dyad, Triad, and Tetrad	Dyad Combination of any two conditions Triad Combination of any three conditions Tetrad and above Combination of any four and above conditions	25 15.2 8.7
Bayes Marin I, 2020⁴⁴	Cardio-metabolic	Cardio-metabolic (Diabetes, Hypertension, Myocardial Infarction, Angina/Stroke)	6.1
Pati S et al, 2021⁴⁷	Dyad	Hypertension and Diabetes	14

as per the availability of information; Dyad: combination of frequently occurring two condition; Triad: combination of frequently occurring three conditions; Tetrad: combination of frequently occurring four conditions; NA: Not Available.

Risk factors

Risk factors for multimorbidity were explored in 23 studies^{5,16,17,18,19,20,22,23,24,27,29,33,35,37,39,40,41,43,46,47} that included age^{16,17,19,23,27,29,33,37,39,43,47}; socioeconomic status ^{19,22,23,29,39}; sex ^19,27,35,39; body mass index (BMI)^24,46; living arrangements (e.g. a traditional joint family system)^27,41; and tobacco¹⁷ and alcohol use²³ (Table 4). Significant positive associations were found for increasing age^{16,17,19,23,27,29,33,37,39}; female sex^19,27,35,39; higher educational attainment^18,19,23,39; affluence^{19,22,23,29,39}; obesity^24,46; and tobacco¹⁷ and alcohol use.²³

Table 4.

Risk factors/determinants/correlates for multimorbidity reported in studies.

Variable	Risk factors^Reference	Comparator/ Reference for calculation of OR/IRR/RRR
Age	Age in years ¹⁶	18-29
	30–39: AOR 4.11; 95% CI: 2.18 to 7.74
	40–49: AOR 7.87; 95% CI: 4.25 to 14.59
	50–59: AOR 16.15; 95% CI: 8.83 to 29.54
	60–69: AOR 23.56; 95% CI: 13.08 to 42.44
	>70: AOR 39.15; 95% CI: 20.72 to 73.98
	Age in years ¹⁷	60-65
	65–70: AOR 2.33; 95% CI: 1.22 to 4.45
	70–75: AOR 4.91; 95% CI: 2.18 to 11.05
	≥75: AOR 4.65; 95% CI: 1.87 to 11.52
	Age in years ¹⁹	18-29
	30-39: AOR 6.10; 95% CI: 3.19 to 11.65
	40-49: AOR 8.49; 95% CI: 4.45 to 16.11
	50-59: AOR 16.41; 95% CI: 8.55 to 31.48
	60-69: AOR 16.14; 95% CI: 8:38 to 31.10
	70-79: AOR 22.35; 95% CI: 11.15 to 44.71
	Age in years ²³	60-70
	≥70: AOR 1.69; 95% CI: 1.52 to 1.8
	Age in years ²⁷	60-64
	≥75: OR 1.76; β coefficient: 0.57
	Age in years ²⁹	50-59
	≥60: IRR 1.38; 95% CI: 1.15 to 1.65
	Age in years ³³	20-39
	≥60: years: AOR 4.96; 95% CI: 1.13 to 21.92
	Age in year ³⁷	30-39
	40-49: AOR 10.7; 95% CI: 2.4 to 47.2
	50-59: AOR 34.6; 95% CI: 7.9 to 149.4
	60-69: AOR 70.1; 95% CI: 16.4 to 298.6
	Participants attending Public facility ³⁹	18-29
	Age in years ³⁹
	30–39: AOR 6.70; 95% CI: 2.89 to 15.57
	40–49: AOR 9.37; 95% CI: 4.05 to 21.64
	50-59: AOR 16.73; 95% CI: 7.12 to 39.31
	60-69: AOR 17.21; 95% CI: 7.35 to 40.28
	≥70: AOR 26.29; 95% CI: 10.52 to 65.66
	Participants attending Private facility ³⁹
	Age in years ³⁹	18-29
	30–39: AOR 6.13; 95% CI: 2.06 to 18.21
	40-49: AOR 8.73; 95% CI: 2.93 to 25.96
	50-59: AOR 19.42; 95% CI: 6.52 to 57.80
	60-69: AOR 16.48; 95% CI: 5.45 to 49.83
	≥70: AOR 20.73; 95% CI: 6.54 to 65.67
	Age in years ⁴³	18-29
	30-39: AOR 2.81; 95% CI: 2.70 to 28.99
	40–49: AOR 11.97; 95% CI: 1.43 to 99.22
	50–59: AOR 19.42; 95% CI: 2.39 to 157.79
	60–69: AOR 37.99; 95% CI: 4.68 to 308.42
	≥70: AOR 42.56; 95% CI: 5.15 to 352.42
	Age in years ⁴⁷
	40-59: ARRR 2.9; 95% CI: 1.6 to 5.1	18-39
	60+: ARRR 6.6; 95% CI: 3.3 to 13.1
Sex	Sex ¹⁹	Male
	Female: AOR 1.61; 95% CI: 1.23 to 2.12
	Sex ²⁷
	Female: OR 2.24; β coefficent: 0.81
	Sex ³⁵
	Female: 65.4%; p value: 0.0007
	Participants attending Public facility ³⁹
	Sex ³⁹
	Female: AOR 1.6;95% CI: 1.11 to 2.27
	Participants attending Private facility ³⁹
	Sex ³⁹
	Females: AOR 1.61; 95% CI:1.07 to 2.42
Caste	Caste ²⁹	Other caste
	SC/ST/OBC: IRR 0.74; 95% CI: 0.61 to 0.92
	Participants attending Public facility ³⁹
	Ethnicity ³⁹Non-aboriginal: AOR 1.56;95% CI: 1.06 to 2.32
Education	Education ¹⁸	No schooling/education
	Primary/middle: IRR 1.4; 95% CI: 1.02 to 1.91
	High school: IRR 1.6; 95% CI: 1.07 to 2.4
	Education ¹⁹
	Primary: AOR 1.61; 95% CI: 1.17 to 2.22
	Secondary and above: AOR 1.53; 95% CI: 1.03 to 2.25
	Education ²³
	Formal schooling: AOR 1.16; 95% CI: 1.04 to 1.30
	Participants attending Private facility ³⁹
	Education ³⁹
	Primary school completed: AOR 2.59; 95% CI: 1.59 to 4.23Secondary schooling and above: AOR 1.99; 95% CI: 1.11 to 3.55
Marital status	Marital status ²²	Multimorbidity versus only one chronic condition
Marital status	Widow:79.2%; p value: 0.0001
Employment	Employment ³⁷
	Unemployed: AOR 1.9; 95% CI: 1.03 to 3.6	Daily wage or self employed
	Occupation ⁴¹	Not engaged
	Engaged: β coefficient 1.8; AOR 5.9; p-value: 0.08
SES	SES ¹⁹	Below poverty line
	APL: AOR 1.35; 95% CI: 1.03 to 1.78	Multimorbidity versus only one condition
	SES ²²	Lowest
	Upper middle: 70.7%; Middle: 74%; p=0.001
	SES ²³	Poor
	Wealthiest: AOR 2.17; 95% CI: 1.81 to 2.59
	SES ²⁹
	Middle: IRR 1.36; 95% CI: 1.07 to 1.73	Below poverty line
	Rich: IRR 1.37; 95% CI: 1.05 to 1.78
	Private facility ³⁹
	SES ³⁹
	APL: AOR 1.35; 95% CI: 1.01 to 2.06
Economic independence	State of economic independence ¹⁷
	Fully dependent: AOR 5.21; 95% CI: 1.99 to 13.60	Not dependent
	Partially dependent: AOR 3.02; 95% CI: 1.57 to 6.81
Habit	Habit ¹⁷
	Chewing tobacco: AOR 2.82; 95% CI: 1.51 to 5.24
	Life style habit ²³
	Alcohol use: AOR 1.23; 95% CI: 1.01 to 1.5	Not chewing tobacco
	Habitual to any lifestyle habit ²⁷	No alcohol use
	Any lifestyle habit: OR 1.44; β coefficent: 0.37	Habitual
BMI and obesity	BMI status Obesity ²⁴
	Obesity: AOR 2.33; 95% CI: 1.35 to 4.02	Normal BMI
	Overweight/obese ⁴⁶	Not over weight
	ObesityARRR:1.61; 95% CI: 1.48 to 1.74
	Waist circumference ⁴⁶	Not high risk
	High risk waist circumference: ARRR:1.66; 95% CI: 1.52 to 1.80
	Waist to hip ratio ⁴⁶	Not high risk
	High-risk wait-hip ratio: ARRR 1.45; 95% CI: 1.33 to 1.59
Self-rated health	Self-rated health ²⁰	Multiple chronic condition versus no chronic condition
Self-rated health	Poor Self-rated health: AOR 2.61; 95% CI:1.44 to 4.72
Family	Family ²²
Family	Nuclear family: 56.7%; Joint families: 69.7%; p=0.003	Multimorbidity versus only one chronic condition
Family History	Family history ⁴⁰	No family history of diabetes
	Diabetes: AOR 1.67; 95% CI: 1.11 to 2.52	No family history of hypertension
	Hypertension: AOR 1.80; 95% CI: 1.29 to 2.50
Living arrangement/ Housing/Living with Family	Living arrangement ²⁷
	Living with married sons: OR 2.24; β coefficient: 0.81	Living alone
	Living with others: OR 2.10; β coefficient: 0.74	95% CI is not given, β coefficient are significant at different p values
	Living Alone ⁴¹	Not alone
	Alone: β coefficient 3.5; AOR 34; p-value: 0.001

APL: Above poverty line; ARRR: Adjusted Relative Risk Ratio; AOR: Adjusted Odds Ratio; BMI: Body Mass Index; SES: Socioeconomic status; IRR: Incidence risk ratio; SC: Scheduled Caste; ST: Scheduled Tribes; OBC: Other backward class; CI: Confidence Interval.

Outcome/consequences

Fourteen studies^{5,18-20,23,30,32,34,36,38,39,42,45,48} reported different health-related consequences associated with multimorbidity (Table 5). The most frequently reported consequences that interplay with multimorbidity was increased healthcare utilization (n=6)^{19,23,30,32,34,39}; reduced health-related quality of life (n=2)^5,34; reduced physical functioning/activities of daily living (ADL) (n=1)¹⁸; mental functioning (n=2)^5,34; poor self-rated health (n=2)^18,20; dissatisfaction about health care system (n=1)³⁰; elderly abuse or mistreatment (n=2)^38,48; and weak handgrip strength/frailty (n=3)^32,36,42. The visit of multimorbid patients to a private clinic was 1.4 times higher than to public health setups.¹⁹ The mean number of hospital visits per patient per year was 1.62 (95% CI, 1.31 to 1.93) and 2.81 (95% CI, 2.34 to 3.28) among patients with single chronic conditions and multimorbidity respectively.¹⁹ The mean number of medicines used increased from 0.56 (95% CI, 0.50 to 0.62) among patients with single chronic conditions to 1.17 (95% CI, 1.04 to 1.29) among patients with multimorbidity.¹⁹ The mean hospital per visit per year in patients with multimorbidity was nine; this is two times the rate of hospitalization among those having one chronic condition.²³

Table 5.

Summary of the relationship between multimorbidity and its patient outcomes (health outcomes, expenditure, and utilization).

Patient Outcomes	Multimorbidity specification	Estimate [95% CI]	Parameterestimate type	Ref.
Out-patient department (OPD) visit (n=2)	Number of chronic conditions (<3 vs ≥3)Multimorbidity vs. not	1.09 [1.01 to 1.17] ↑2.81 [2.34 to 3.28] vs. 1.62 [1.31 to 1.93]↑	AOR^*Mean	3234
Hospitalization (n=1)	Multimorbidity vs. notMultimorbidity vs. not	9 vs. 3↑2.32 [1.82 to 2.95] ↑	MeanAOR	2323
Facility (n=1)	Private vs. Public	1.40 [1.08 to 1.81] ↑	AOR	19
Medicine taken(n=3)	Number of chronic conditions (<3 vs ≥3)Multimorbidity vs. notPrivate vs. Public	1.41 [1.27 to 1.56] ↑1.17 [1.04 to 1.29] vs. 0.56 [0.50 to 0.62]↑2.7 vs. 1.2↑	AORMeanMean	391939
Health care expenditure/Out of pocket expenditure (OOPE) (n=1)	Physical conditions with Psychiatric conditions vs. only Psychiatric conditions	INR 12219 vs. INR 4414↑	Mean	45
Frailty (n=2)	Age <50 years vs. >50 yearsMultimorbidity vs. not	4.45 [1.51 to 13.05] ↑1.93 [1.61 to 2.24] ↑	AOROR	3242
Decision making in health care (n=1)	Men vs. Women	1.30 [1.12 to 1.91] vs. 1.02 [0.89 to 1.17] ↑	AOR	30
Dissatisfied about health care system (n=1)	Men vs. Women	1.40 [1.15 to 3.28] vs. 1.10 [1.07 to 1.29] ↑	AOR	30
Elderly abuse or mistreatment (n=2)	Two chronic conditionsthree, and four or more chronic conditionsMore chronic diseasesMultimorbidity vs. not	3.02 [2.33 to 3.91] ↑4.16 [3.02 to 5.74] ↑5.06 [3.50 to 7.31] ↑1.68 [1.11 to 2.57] ↑	AORAORAORAOR	38383848
Health-related quality of life (HRQoL) (n=2)PhysicalPhysicalMental	Multimorbidity vs. notMultimorbidity vs. notMultimorbidity vs. notMultimorbidity vs. not	43.23 [42.62 to 43.84] vs. 43.69[43.35 to 44.03]↓41.07 vs. 42.4841.58 [40.74 to 42.43] vs. 44.52 [43.96 to 45.08]↓40.79 vs. 44.14 ↓	MeanMeanMeanMean	345345
Activities of daily living (ADL) (n=1)	Multimorbidity vs. not	6.9 [4.8 to 10.3] ↑	Logit model, OR	18
Self-rated health (n=2)	Multimorbidity vs. notMultimorbidity vs. not	15.1 [9.5 to 23.9] ↑2.6 [1.44 to 4.72] ↑	Logit model, OROR	1820
Weak handgrip strength (n=1)	Two conditionsThree conditionsFour conditions	1.29 [1.11 to 1.50] ↑1.41 [1.18 to 1.68] ↑1.78 [1.46 to 2.18] ↑	OROROR	363636
Higher care challenges (n=1)	Multimorbidity vs. not	1.48 [1.01 to 2.05] ↑	AOR	45

OR: Odds ratio, AOR: Adjusted odds ratio. All reported outcomes are statistically significant.

Discussion

Summary of evidence

This systematic review provides comprehensive evidence on the burden of multimorbidity in India. The prevalence of multimorbidity was observed to vary from 1.16% to 65.9%. Nonetheless, the retrieved studies were heterogeneous in the characteristics while the prevalence was quite similar between the different subgroups i.e., nationally representative samples and samples from different provinces in India. The prevalence was high among participants aged 60 years and above (range 30.7% to 65.9%). The studies which were planned a-priori focused on more number of chronic conditions (median 18) whereas the median conditions considered in studies based on secondary data was 9 while the overall median number of diseases considered was 12.

Comparison with existing literature

There is considerable heterogeneity among the measures used to assess multimorbidity in terms of number of diseases considered. In this review, the median numbers of diseases identified were 12 which is lower than the median number [Median: 17 (IQR: 11-23)] of conditions reported by a systematic review of 566 studies on multimorbidity.⁵¹ Additionally, there was a variance in the median number of conditions considered in primary and secondary studies. A probable reason for the wide variance between the two is that the latter does not intend to assess multimorbidity rather the data is extrapolated to do so due to a lack of nationwide data pertaining to multimorbidity. Hence, there is an urgent need towards building a consensus on the number of diseases or groups to be included to assess multimorbidity which would help in improving inter-regional comparisons.⁵² Moreover, LMICs such as India should plan to conduct nation-wide surveys on multimorbidity to elicit its real burden.⁵³ Here, the findings of a systematic review of systematic reviews is worth considering that recommended meaure selection (eg., using a cut-off of two or more conditions) be used for tools validated for the outcome of interest, and where there is no validated measures, or where multiple outcomes or populations are being considered, disease count should be used as an alternative.⁵⁴ Additionally, debates around the consideration of definition for complex multimorbidity is also worth considering where a study suggests that complex multimorbidity must consider complexity of conditions rather than simple count of diseases across body systems.⁵⁵

We observed that the population aged 60 years and above had a higher prevalence (range 30.7% to 65.9%) of multimorbidity which is in contrast with the findings reported from China⁵⁶ which showed a very wide range (6.4% to 76.5%), though the latter was based on a narrative review. However, the pooled prevalence of multiple chronic illnesses reported in Caribbean and Latin American countries⁵⁷ was 47% which is higher than the pooled prevalence reported in our review. An Australian study⁵⁸ reported 52% prevalence which is also higher than the prevalence of multimorbidity in India. The presence of clusters of conditions or complexity (the number of domains present) increased with age. Similar findings were also observed from another study in Australia⁵⁹ where 83.2% of the surveyed participants aged 75 years or older had multimorbidity; with 58.2% having morbidity in three or more domains, and 33.4% in four or more. As the ageing population increases, multimorbidity will continue as a major health problem in the years to come⁶⁰ which needs urgent action.

In this review, we observed an increase in the health care cost and utilization which is consistent with the findings of Bori et al⁶¹ in UK. Only twelve studies reported patterns/ clusters/disease combinations but it indicated a shift in the conceptualization of multimorbidity from purely disease count to specific disease combinations/clusters. Additionally, we also noted the focus on specific age groups to be a trend in this review. These findings are consistent with the report of Bori et al.⁶¹ Nonetheless, it is worth mentioning that these findings were not elicited by an earlier systematic review conducted among South Asian population¹⁰ as the term multimorbidity was not defined in the MeSH (Medical Subject Headings of PubMed) terminology at that time.

Multimorbidity assessment largely depends on considering the simple count of diseases, however future studies should explore the most common combinations and frequently occurring clusters of diseases that could help in providing insights for the complex care needs of patients with multimorbidity. The health research as well as practice has conventionally focused on single-disease framework rather than multiple conditions. Therefore, a complementary strategy is needed, supporting general physicians to provide personalized patient centered, comprehensive continuity of care, especially in socioeconomically deprived areas.⁶² Additionally, future studies eliciting the burden of multimorbidity among vulnerable populations such as urban poor, and tribal are warranted. Social determinants of health are equally relevant for chronic conditions and studies report that socio-economic marginalization increases the risk of multimorbidity.^63,64 Furthermore, the difficulties for these individuals are exacerbated by the ‘inverse care law’ which states that the rising demand for healthcare access leads to fewer consultations, decreased patient enablement and increased physician stress.⁶⁵ Hence, these factors highlight the importance of estimating the burden of chronic conditions and identifying their care seeking pathway so as to make the existing programmes more equitable and design future policies based on the evidence. Nonetheless, few studies have been published covering these vulnerable groups which were beyond the date of last search of this review.^66-74

We also observed that studies were mostly conducted among patients attending out-patient department of primary care though no studies document the prevalence of multimorbidity among in-patients (IPD). A major gap exists in enumerating the care seeking pathway of these multimorbid individuals which may support in further taking steps towards primary care strengthening. We observed Multimorbidity Assessment Questionnaire for Primary Care (MAQ-PC) was the only validated tool which was designed to explicitly assess multimorbidity in India.²⁵ With a lack of gold standard, this tool has been developed following an iterative process that can be used for undertaking multimorbidity studies in future.

We observed affluence to be associated with multimorbidity which is consistent with the findings of a systematic review on multimorbidity in LMICS that observed an increased risk of NCD multimorbidity [Pooled OR from 10 studies: 1.35 (95% CI: 1.02 to 1.80)] among well-off individuals.⁷⁵ A possible explanation to this could be that affluent group has a higher capacity to pay for healthcare services which lead to better diagnosis and hence, self-report of multimorbidity. However, these findings differ from that of high-income countries where deprivation is associated with multimorbidity as suggested by a systematic review.⁷⁶ Additionally, most studies reported that the individuals with more years of schooling (either primary or secondary schooling) had a higher chance of having multimorbidity as compared to those with no formal education which is consistent with the findings of a systematic review that included studies from Southeast Asia.⁷² A probable reason for this could be that with education, people tend to be more health conscious, and hence have better chances of diagnosis and self-report of chronic conditions.

Implications for research and practice

India is a federal union comprising of 28 states and 8 union territories, forming a total of 36 entities. The studies on multimorbidity within a state are very limited preventing the assessment of the actual burden in the country. The present evidence may be considered as a tip of the Iceberg. More studies specific to different zones of the country, state/province is required to assess the real burden of multimorbidity in India. India is a country of medical pluralism wherein apart from conventional medicine system, traditional and alternative system of medicine popularly known as Ayurveda, Yoga and Naturopathy, Unani, Siddha, and Homeopathy (AYUSH) are also instrumental in contributing to health care. AYUSH care utilization is higher among patients with chronic diseases and also for treating skin-related and musculo-skeletal ailments.⁷⁰ The current epidemiological transition has been a major driver impelling a radical rethink of the structure of health care, especially concerning the role, quality and capacity of primary health care. The development of new clinical care guidelines should be addressed to focus on multiple systems and not just only on one disease. Sensitization of frontline workers such as Accredited Social Health Activist (ASHA) and Community Health Officer (CHO) towards the needs of multimorbid individuals is required with a focus on family-level interventions for shared risk factors.⁷⁷ Capacity building of primary care staff to manage multimorbidity is also needed. Nonetheless, most LMICs face a similar healthcare challenge in combating multimorbidity where the determinants, patient outcomes as well as impact of multimorbidity on health system remain alike. Hence, the findings of this study could also be extrapolated to countries with similar demographic and epidemiological transition along with similar health systems by targeting these determinants.

Strengths and limitations

We employed a comprehensive search strategy in various databases to find out exhaustive list of all available articles in the domain. Further, screening, data extraction and analysis were performed following prospectively registered protocol. Meta-analysis was done to synthesize the pooled prevalence which was a major strength of this study. However, heterogeneity and repetition in data led us to include only 19 articles in the meta-analysis. This review includes sample from both community and primary care set ups which is an additional strength. However, the fact that this study is restricted to India alone may be seen as a drawback. Nonetheless, India is the second-most populous nation in the world, hence it was imperative to perform this review for future policy decisions.

Conclusion

Multimorbidity has become a norm in India. The increasing burden of multimorbidity among older adults cannot be overlooked. Nation-wide primary studies to investigate the real burden of multimorbidity are warranted. Additionally, studies pertaining to vulnerable groups such as urban poor and tribal along with interventions are also warranted. Uniform methods to assess multimorbidity are required.

Supplemental Material

Supplemental Material - Epidemiology and impact of chronic disease multimorbidity in India: a systematic review and meta-analysis

Supplemental Material for Epidemiology and impact of chronic disease multimorbidity in India: a systematic review and meta-analysis by Roja Varanasi, Abhinav Sinha, Meenakshi Bhatia, Debadatta Nayak, Raj K Manchanda, Rajeev Janardhanan, John Tayu Lee, Simran Tandon, Sanghamitra Pati in Journal of Multimorbidity and Comorbidity

Footnotes

Acknowledgements

We thank Dr. Banamber Sahoo, Library and Information Officer, ICMR-Regional Medical Research Centre, Bhubaneswar for his support in developing search strategy and providing access to databases. We are also thankful to Dr. Srikanta Kanungo, Scientist-D, ICMR-Regional Medical Research Centre, Bhubaneswar for helping us with the meta-analysis.

Author contributions

Concept and design: RV, AS and SP. Acquisition, analysis, or interpretation of data: RV, AS, MB, DN, RKM, RJ, ST, JTL and SP. Drafting of the manuscript: RV, AS, DN and SP. Critical revision of the manuscript for important intellectual content: MB, RKM, RJ, JTL and ST. Statistical analysis: RV and AS. Administrative and technical support: RJ, ST and SP. Supervision: SP. All authors have agreed on publishing the final version of manuscript.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Correction(August 2024):

Article updated to include department name “Faculty of Medical & Health Sciences” in the affiliation of author “Rajiv Janardhanan”. The revised affiliation is “Faculty of Medical & Health Sciences, SRM Institute of Science & Technology, Kattankulathur, India”.

Ethical statement

ORCID iD

Abhinav Sinha

Data availability statement

All data underlying this research will be made available on reasonable request to the authors. *

Supplemental Material

Supplemental material for this article is available online.

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